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To mix or not to mix: comparing the predictive performance of mixture models vs. separate species distribution models
Author(s) -
Hui Francis K. C.,
Warton David I.,
Foster Scott D.,
Dunstan Piers K.
Publication year - 2013
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1890/12-1322.1
Subject(s) - species distribution , computer science , exploit , ecology , distribution (mathematics) , predictive modelling , environmental niche modelling , biological system , data mining , machine learning , biology , mathematics , ecological niche , habitat , mathematical analysis , computer security
Species distribution models (SDMs) are an important tool for studying the patterns of species across environmental and geographic space. For community data, a common approach involves fitting an SDM to each species separately, although the large number of models makes interpretation difficult and fails to exploit any similarities between individual species responses. A recently proposed alternative that can potentially overcome these difficulties is species archetype models (SAMs), a model‐based approach that clusters species based on their environmental response. In this paper, we compare the predictive performance of SAMs against separate SDMs using a number of multi‐species data sets. Results show that SAMs improve model accuracy and discriminatory capacity compared to separate SDMs. This is achieved by borrowing strength from common species having higher information content. Moreover, the improvement increases as the species become rarer.